Overview

Dataset statistics

Number of variables13
Number of observations114
Missing cells16
Missing cells (%)1.1%
Duplicate rows36
Duplicate rows (%)31.6%
Total size in memory11.7 KiB
Average record size in memory105.1 B

Variable types

Numeric12
Unsupported1

Alerts

Dataset has 36 (31.6%) duplicate rowsDuplicates
occupiedDwellings is highly correlated with apartmentLessThanFiveStories and 1 other fieldsHigh correlation
singleDetached is highly correlated with averageNumRoomsHigh correlation
semiDetached is highly correlated with averageValueHigh correlation
apartmentLessThanFiveStories is highly correlated with occupiedDwellingsHigh correlation
apartmentMoreThanFiveStories is highly correlated with occupiedDwellings and 1 other fieldsHigh correlation
averageNumRooms is highly correlated with singleDetached and 2 other fieldsHigh correlation
averageValue is highly correlated with semiDetachedHigh correlation
medianHouseholdIncome is highly correlated with averageNumRoomsHigh correlation
occupiedDwellings is highly correlated with apartmentLessThanFiveStoriesHigh correlation
singleDetached is highly correlated with averageNumRoomsHigh correlation
apartmentLessThanFiveStories is highly correlated with occupiedDwellingsHigh correlation
apartmentMoreThanFiveStories is highly correlated with averageNumRoomsHigh correlation
averageNumRooms is highly correlated with singleDetached and 2 other fieldsHigh correlation
averageValue is highly correlated with medianHouseholdIncomeHigh correlation
medianHouseholdIncome is highly correlated with averageNumRooms and 1 other fieldsHigh correlation
occupiedDwellings is highly correlated with apartmentLessThanFiveStoriesHigh correlation
apartmentLessThanFiveStories is highly correlated with occupiedDwellingsHigh correlation
averageNumRooms is highly correlated with medianHouseholdIncomeHigh correlation
medianHouseholdIncome is highly correlated with averageNumRoomsHigh correlation
df_index is highly correlated with occupiedDwellings and 8 other fieldsHigh correlation
occupiedDwellings is highly correlated with df_index and 9 other fieldsHigh correlation
singleDetached is highly correlated with df_index and 10 other fieldsHigh correlation
semiDetached is highly correlated with df_index and 7 other fieldsHigh correlation
rowHouses is highly correlated with df_index and 7 other fieldsHigh correlation
apartmentInDuplex is highly correlated with df_index and 8 other fieldsHigh correlation
apartmentLessThanFiveStories is highly correlated with df_index and 9 other fieldsHigh correlation
apartmentMoreThanFiveStories is highly correlated with df_index and 7 other fieldsHigh correlation
otherDwellings is highly correlated with df_index and 6 other fieldsHigh correlation
averageNumRooms is highly correlated with df_index and 10 other fieldsHigh correlation
averageValue is highly correlated with singleDetached and 2 other fieldsHigh correlation
medianHouseholdIncome is highly correlated with occupiedDwellings and 5 other fieldsHigh correlation
averageNumRooms has 4 (3.5%) missing values Missing
averageValue has 4 (3.5%) missing values Missing
medianHouseholdIncome has 4 (3.5%) missing values Missing
medianRent has 4 (3.5%) missing values Missing
medianRent is an unsupported type, check if it needs cleaning or further analysis Unsupported
df_index has 3 (2.6%) zeros Zeros
semiDetached has 7 (6.1%) zeros Zeros
rowHouses has 23 (20.2%) zeros Zeros
apartmentInDuplex has 2 (1.8%) zeros Zeros
apartmentMoreThanFiveStories has 25 (21.9%) zeros Zeros
otherDwellings has 49 (43.0%) zeros Zeros

Reproduction

Analysis started2021-12-06 18:09:44.540455
Analysis finished2021-12-06 18:10:13.204710
Duration28.66 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct38
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.5
Minimum0
Maximum37
Zeros3
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T14:10:13.325537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.65
Q19
median18.5
Q328
95-th percentile35.35
Maximum37
Range37
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.01427071
Coefficient of variation (CV)0.5953659844
Kurtosis-1.201545472
Mean18.5
Median Absolute Deviation (MAD)9.5
Skewness0
Sum2109
Variance121.3141593
MonotonicityNot monotonic
2021-12-06T14:10:13.510050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
03
 
2.6%
283
 
2.6%
213
 
2.6%
223
 
2.6%
233
 
2.6%
243
 
2.6%
253
 
2.6%
263
 
2.6%
273
 
2.6%
293
 
2.6%
Other values (28)84
73.7%
ValueCountFrequency (%)
03
2.6%
13
2.6%
23
2.6%
33
2.6%
43
2.6%
53
2.6%
63
2.6%
73
2.6%
83
2.6%
93
2.6%
ValueCountFrequency (%)
373
2.6%
363
2.6%
353
2.6%
343
2.6%
333
2.6%
323
2.6%
313
2.6%
303
2.6%
293
2.6%
283
2.6%

occupiedDwellings
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct68
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1763.991228
Minimum665
Maximum3265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T14:10:13.684877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum665
5-th percentile900
Q11151.25
median1707.5
Q32251.25
95-th percentile2849.25
Maximum3265
Range2600
Interquartile range (IQR)1100

Descriptive statistics

Standard deviation647.5126608
Coefficient of variation (CV)0.3670724948
Kurtosis-0.8340030349
Mean1763.991228
Median Absolute Deviation (MAD)557.5
Skewness0.3076195824
Sum201095
Variance419272.6459
MonotonicityNot monotonic
2021-12-06T14:10:13.886649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11554
 
3.5%
24304
 
3.5%
23504
 
3.5%
10954
 
3.5%
9004
 
3.5%
19003
 
2.6%
6653
 
2.6%
16953
 
2.6%
27652
 
1.8%
18752
 
1.8%
Other values (58)81
71.1%
ValueCountFrequency (%)
6653
2.6%
8701
 
0.9%
9004
3.5%
9351
 
0.9%
9601
 
0.9%
9751
 
0.9%
10101
 
0.9%
10252
1.8%
10302
1.8%
10551
 
0.9%
ValueCountFrequency (%)
32652
1.8%
30452
1.8%
28852
1.8%
28301
0.9%
27702
1.8%
27652
1.8%
26552
1.8%
26051
0.9%
25651
0.9%
25301
0.9%

singleDetached
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct71
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean563.9813596
Minimum5
Maximum1885
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T14:10:14.079393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile15
Q1380
median557.3
Q3753.26625
95-th percentile1052.17125
Maximum1885
Range1880
Interquartile range (IQR)373.26625

Descriptive statistics

Standard deviation360.725422
Coefficient of variation (CV)0.6396052207
Kurtosis2.084772348
Mean563.9813596
Median Absolute Deviation (MAD)194.9575
Skewness0.7743304359
Sum64293.875
Variance130122.8301
MonotonicityNot monotonic
2021-12-06T14:10:14.275367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5256
 
5.3%
8204
 
3.5%
4354
 
3.5%
7004
 
3.5%
10052
 
1.8%
2452
 
1.8%
1152
 
1.8%
8802
 
1.8%
7502
 
1.8%
4702
 
1.8%
Other values (61)84
73.7%
ValueCountFrequency (%)
52
1.8%
9.631
0.9%
10.151
0.9%
10.6751
0.9%
152
1.8%
202
1.8%
302
1.8%
34.561
0.9%
802
1.8%
80.321
0.9%
ValueCountFrequency (%)
18852
1.8%
1579.21
0.9%
11752
1.8%
1139.7751
0.9%
10052
1.8%
9552
1.8%
9452
1.8%
935.221
0.9%
925.31
0.9%
890.0551
0.9%

semiDetached
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct58
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.69745614
Minimum0
Maximum315
Zeros7
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T14:10:14.512205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115.015
median42.76
Q3110
95-th percentile246.675
Maximum315
Range315
Interquartile range (IQR)94.985

Descriptive statistics

Standard deviation81.2290373
Coefficient of variation (CV)1.032168526
Kurtosis0.6974312602
Mean78.69745614
Median Absolute Deviation (MAD)32.6325
Skewness1.262884145
Sum8971.51
Variance6598.1565
MonotonicityNot monotonic
2021-12-06T14:10:14.697313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
308
 
7.0%
158
 
7.0%
07
 
6.1%
256
 
5.3%
56
 
5.3%
354
 
3.5%
604
 
3.5%
954
 
3.5%
1902
 
1.8%
502
 
1.8%
Other values (48)63
55.3%
ValueCountFrequency (%)
07
6.1%
56
5.3%
9.4951
 
0.9%
9.751
 
0.9%
102
 
1.8%
10.2551
 
0.9%
10.3051
 
0.9%
14.91
 
0.9%
14.961
 
0.9%
158
7.0%
ValueCountFrequency (%)
3152
1.8%
2952
1.8%
260.131
0.9%
259.351
0.9%
239.851
0.9%
2302
1.8%
2152
1.8%
2102
1.8%
190.081
0.9%
1902
1.8%

rowHouses
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct55
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.27223684
Minimum0
Maximum455
Zeros23
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T14:10:14.879063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.8125
median39.95
Q3129.41
95-th percentile376.575
Maximum455
Range455
Interquartile range (IQR)119.5975

Descriptive statistics

Standard deviation125.8440418
Coefficient of variation (CV)1.334900348
Kurtosis1.285923317
Mean94.27223684
Median Absolute Deviation (MAD)39.95
Skewness1.556324923
Sum10747.035
Variance15836.72285
MonotonicityNot monotonic
2021-12-06T14:10:15.118630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
023
 
20.2%
156
 
5.3%
54
 
3.5%
254
 
3.5%
554
 
3.5%
104
 
3.5%
3752
 
1.8%
452
 
1.8%
702
 
1.8%
652
 
1.8%
Other values (45)61
53.5%
ValueCountFrequency (%)
023
20.2%
54
 
3.5%
9.4951
 
0.9%
9.751
 
0.9%
104
 
3.5%
10.111
 
0.9%
10.261
 
0.9%
156
 
5.3%
15.481
 
0.9%
202
 
1.8%
ValueCountFrequency (%)
4552
1.8%
434.71
0.9%
4302
1.8%
379.51
0.9%
3752
1.8%
3402
1.8%
3352
1.8%
324.2251
0.9%
299.981
0.9%
269.691
0.9%

apartmentInDuplex
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct67
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.1997368
Minimum0
Maximum555
Zeros2
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T14:10:15.500415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.34975
Q145
median92.5
Q3175
95-th percentile338.66625
Maximum555
Range555
Interquartile range (IQR)130

Descriptive statistics

Standard deviation112.7504316
Coefficient of variation (CV)0.8934284209
Kurtosis3.914916875
Mean126.1997368
Median Absolute Deviation (MAD)52.5
Skewness1.811055159
Sum14386.77
Variance12712.65983
MonotonicityNot monotonic
2021-12-06T14:10:16.455802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
906
 
5.3%
456
 
5.3%
754
 
3.5%
404
 
3.5%
2104
 
3.5%
1754
 
3.5%
154
 
3.5%
102
 
1.8%
1052
 
1.8%
3902
 
1.8%
Other values (57)76
66.7%
ValueCountFrequency (%)
02
1.8%
9.631
 
0.9%
102
1.8%
10.6751
 
0.9%
14.791
 
0.9%
154
3.5%
24.71
 
0.9%
252
1.8%
302
1.8%
30.31
 
0.9%
ValueCountFrequency (%)
5552
1.8%
534.0251
0.9%
415.531
0.9%
3902
1.8%
311.0251
0.9%
3102
1.8%
2602
1.8%
250.5151
0.9%
250.0351
0.9%
2402
1.8%

apartmentLessThanFiveStories
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean530.1420614
Minimum45.24
Maximum1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T14:10:16.915065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum45.24
5-th percentile125.9125
Q1300
median507.8625
Q3720
95-th percentile986.285
Maximum1010
Range964.76
Interquartile range (IQR)420

Descriptive statistics

Standard deviation261.6592034
Coefficient of variation (CV)0.493564315
Kurtosis-0.9152869322
Mean530.1420614
Median Absolute Deviation (MAD)207.8625
Skewness0.121627664
Sum60436.195
Variance68465.53871
MonotonicityNot monotonic
2021-12-06T14:10:17.514215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3006
 
5.3%
10004
 
3.5%
2502
 
1.8%
6952
 
1.8%
8952
 
1.8%
10102
 
1.8%
6252
 
1.8%
7202
 
1.8%
7252
 
1.8%
4552
 
1.8%
Other values (63)88
77.2%
ValueCountFrequency (%)
45.241
0.9%
602
1.8%
902
1.8%
99.751
0.9%
1402
1.8%
155.521
0.9%
160.51
0.9%
1702
1.8%
230.41
0.9%
244.7251
0.9%
ValueCountFrequency (%)
10102
1.8%
10004
3.5%
978.91
 
0.9%
974.41
 
0.9%
954.271
 
0.9%
935.1951
 
0.9%
9252
1.8%
920.011
 
0.9%
8952
1.8%
8702
1.8%

apartmentMoreThanFiveStories
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct58
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean351.6162719
Minimum0
Maximum1580
Zeros25
Zeros (%)21.9%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T14:10:18.116501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q127.7625
median255.015
Q3573.06
95-th percentile1110
Maximum1580
Range1580
Interquartile range (IQR)545.2975

Descriptive statistics

Standard deviation376.6559946
Coefficient of variation (CV)1.071213208
Kurtosis1.216025306
Mean351.6162719
Median Absolute Deviation (MAD)250.015
Skewness1.289507502
Sum40084.255
Variance141869.7383
MonotonicityNot monotonic
2021-12-06T14:10:18.668881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
025
 
21.9%
3254
 
3.5%
5754
 
3.5%
52
 
1.8%
502
 
1.8%
6102
 
1.8%
3502
 
1.8%
6752
 
1.8%
752
 
1.8%
1502
 
1.8%
Other values (48)67
58.8%
ValueCountFrequency (%)
025
21.9%
52
 
1.8%
15.361
 
0.9%
25.351
 
0.9%
352
 
1.8%
502
 
1.8%
64.91
 
0.9%
752
 
1.8%
80.841
 
0.9%
902
 
1.8%
ValueCountFrequency (%)
15802
1.8%
1370.461
0.9%
11802
1.8%
11102
1.8%
10802
1.8%
1064.451
0.9%
1009.471
0.9%
980.491
0.9%
899.871
0.9%
8852
1.8%

otherDwellings
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.95241228
Minimum0
Maximum150
Zeros49
Zeros (%)43.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T14:10:18.871638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q320
95-th percentile96.71375
Maximum150
Range150
Interquartile range (IQR)20

Descriptive statistics

Standard deviation34.32012456
Coefficient of variation (CV)1.81085785
Kurtosis5.668525736
Mean18.95241228
Median Absolute Deviation (MAD)5
Skewness2.45941641
Sum2160.575
Variance1177.87095
MonotonicityNot monotonic
2021-12-06T14:10:19.229534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
049
43.0%
518
 
15.8%
208
 
7.0%
104
 
3.5%
754
 
3.5%
354
 
3.5%
154
 
3.5%
652
 
1.8%
1202
 
1.8%
1502
 
1.8%
Other values (17)17
 
14.9%
ValueCountFrequency (%)
049
43.0%
518
 
15.8%
9.4251
 
0.9%
9.4951
 
0.9%
9.61
 
0.9%
9.8251
 
0.9%
104
 
3.5%
10.041
 
0.9%
10.081
 
0.9%
10.6751
 
0.9%
ValueCountFrequency (%)
1502
1.8%
149.641
 
0.9%
124.7451
 
0.9%
1202
1.8%
84.1751
 
0.9%
754
3.5%
70.111
 
0.9%
652
1.8%
60.721
 
0.9%
54.811
 
0.9%

averageNumRooms
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct37
Distinct (%)33.6%
Missing4
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean5.773636364
Minimum3.5
Maximum8.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T14:10:19.664846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile4
Q15.3
median5.6
Q36.5
95-th percentile7.365
Maximum8.5
Range5
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.04694201
Coefficient of variation (CV)0.1813314771
Kurtosis-0.1708518958
Mean5.773636364
Median Absolute Deviation (MAD)0.7
Skewness0.08962215487
Sum635.1
Variance1.096087573
MonotonicityNot monotonic
2021-12-06T14:10:19.823115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5.69
 
7.9%
5.48
 
7.0%
6.57
 
6.1%
5.37
 
6.1%
6.86
 
5.3%
65
 
4.4%
7.25
 
4.4%
5.55
 
4.4%
5.75
 
4.4%
5.24
 
3.5%
Other values (27)49
43.0%
(Missing)4
 
3.5%
ValueCountFrequency (%)
3.51
 
0.9%
3.72
1.8%
3.92
1.8%
43
2.6%
4.11
 
0.9%
4.21
 
0.9%
4.31
 
0.9%
4.43
2.6%
4.53
2.6%
4.72
1.8%
ValueCountFrequency (%)
8.51
 
0.9%
8.22
 
1.8%
7.71
 
0.9%
7.52
 
1.8%
7.25
4.4%
7.12
 
1.8%
72
 
1.8%
6.91
 
0.9%
6.86
5.3%
6.71
 
0.9%

averageValue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct74
Distinct (%)67.3%
Missing4
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean297805.7182
Minimum116910
Maximum723201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T14:10:19.986022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum116910
5-th percentile150899.15
Q1225183.25
median270969
Q3343631.25
95-th percentile525779
Maximum723201
Range606291
Interquartile range (IQR)118448

Descriptive statistics

Standard deviation125633.1459
Coefficient of variation (CV)0.4218627724
Kurtosis2.992525713
Mean297805.7182
Median Absolute Deviation (MAD)62095.5
Skewness1.567696826
Sum32758629
Variance1.578368735 × 1010
MonotonicityNot monotonic
2021-12-06T14:10:20.250489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1514312
 
1.8%
3024762
 
1.8%
3402842
 
1.8%
2796122
 
1.8%
1555352
 
1.8%
2412612
 
1.8%
4020592
 
1.8%
2407842
 
1.8%
3043302
 
1.8%
2943162
 
1.8%
Other values (64)90
78.9%
(Missing)4
 
3.5%
ValueCountFrequency (%)
1169101
0.9%
1208721
0.9%
1365261
0.9%
1424991
0.9%
1481001
0.9%
1504641
0.9%
1514312
1.8%
1555352
1.8%
1759111
0.9%
1782271
0.9%
ValueCountFrequency (%)
7232012
1.8%
7209892
1.8%
5581331
0.9%
5257792
1.8%
4986732
1.8%
4826092
1.8%
4790641
0.9%
4470791
0.9%
4139901
0.9%
4025952
1.8%

medianHouseholdIncome
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct74
Distinct (%)67.3%
Missing4
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean51699.84545
Minimum24784
Maximum126261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T14:10:20.425572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum24784
5-th percentile28665.5
Q140858
median47108
Q360993.25
95-th percentile80018
Maximum126261
Range101477
Interquartile range (IQR)20135.25

Descriptive statistics

Standard deviation18090.55011
Coefficient of variation (CV)0.3499149746
Kurtosis5.08726132
Mean51699.84545
Median Absolute Deviation (MAD)8298
Skewness1.788731864
Sum5686983
Variance327268003.3
MonotonicityNot monotonic
2021-12-06T14:10:20.640331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800182
 
1.8%
497282
 
1.8%
470532
 
1.8%
549022
 
1.8%
452182
 
1.8%
610822
 
1.8%
415942
 
1.8%
646512
 
1.8%
673832
 
1.8%
427592
 
1.8%
Other values (64)90
78.9%
(Missing)4
 
3.5%
ValueCountFrequency (%)
247841
0.9%
248001
0.9%
267221
0.9%
278461
0.9%
283822
1.8%
290121
0.9%
307932
1.8%
328702
1.8%
342981
0.9%
354311
0.9%
ValueCountFrequency (%)
1262612
1.8%
1177381
0.9%
844282
1.8%
800182
1.8%
748562
1.8%
743821
0.9%
721761
0.9%
720032
1.8%
705391
0.9%
673832
1.8%

medianRent
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing4
Missing (%)3.5%
Memory size1.0 KiB

Interactions

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2021-12-06T14:10:10.030752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-12-06T14:10:20.843341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-06T14:10:21.245730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-06T14:10:21.679434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-06T14:10:22.090037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-06T14:10:12.180582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-06T14:10:12.556283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-12-06T14:10:12.778516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-12-06T14:10:13.041395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexoccupiedDwellingssingleDetachedsemiDetachedrowHousesapartmentInDuplexapartmentLessThanFiveStoriesapartmentMoreThanFiveStoriesotherDwellingsaverageNumRoomsaverageValuemedianHouseholdIncomemedianRent
002385720.27081.090131.17531.005810.900615.3300.0005.9257003.046273.0701
112045449.900169.735130.88085.890615.545474.440124.7455.8179541.040235.0650
222565890.055164.16010.260415.530705.375379.6200.0005.7178227.041043.0665
33975629.8509.7509.75060.450244.72525.3500.0007.2344747.061874.0647
441320545.160190.08025.08089.760324.720145.2000.0006.7242734.062928.0722
552830783.91079.240299.980234.890749.950679.2000.0005.7187471.047163.0685
661965575.74535.370324.225135.585520.725379.2459.8255.6211287.035702.0703
771920764.16030.7200.000115.200430.080574.0800.0005.5262096.038319.0683
882300354.200140.300434.700220.800749.800391.0000.0005.3136526.034298.0637
991440750.240149.760234.720145.440155.5200.00010.0806.8148100.041323.0475

Last rows

df_indexoccupiedDwellingssingleDetachedsemiDetachedrowHousesapartmentInDuplexapartmentLessThanFiveStoriesapartmentMoreThanFiveStoriesotherDwellingsaverageNumRoomsaverageValuemedianHouseholdIncomemedianRent
104281695750.040.05.0130.0460.0295.020.05.4241927.044958.0672.0
105292075945.025.060.025.0445.0575.00.06.5293761.061082.01011.0
106302180600.070.070.0205.01010.0215.010.05.4270969.044621.0740.0
107311730470.0110.025.0125.0700.0150.0150.05.3206460.041594.0750.0
108321990880.0315.085.090.0625.00.00.06.5239150.060602.0683.0
109331270525.0230.055.090.0295.00.075.06.1155535.049728.0706.0
11034900700.065.00.010.060.05.065.07.2192841.064651.0734.0
1113532651885.0100.0375.045.0525.0325.00.07.5302476.084428.01309.0
11236153520.015.010.015.0400.01080.00.0NaNNaNNaNNaN
11337216015.05.020.015.01000.01110.00.0NaNNaNNaNNaN

Duplicate rows

Most frequently occurring

df_indexoccupiedDwellingssingleDetachedsemiDetachedrowHousesapartmentInDuplexapartmentLessThanFiveStoriesapartmentMoreThanFiveStoriesotherDwellingsaverageNumRoomsaverageValuemedianHouseholdIncome# duplicates
002430700.095.0145.045.0870.0575.00.05.8283020.045134.02
112095455.0175.0135.075.0650.0480.0120.05.7230158.041444.02
2230451005.0210.025.0390.0735.0675.00.05.6232374.048312.02
331155730.030.00.055.0300.035.00.07.2402595.074856.02
441455615.0215.040.090.0300.0195.00.06.6290299.065536.02
552885820.075.0340.0260.0710.0670.05.05.6253840.056099.02
661945525.015.0335.0160.0535.0370.05.05.4240784.036344.02
771900795.030.00.085.0415.0570.00.05.5340284.046551.02
882350380.0190.0430.0175.0725.0445.05.05.2184205.040858.02
991495820.0155.0220.0120.0170.00.05.06.8241261.050193.02